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It is expected that artificial intelligence reduces labor and improves productivity of the shield tunneling, which is one of tunnel construction methods. In a planning process of the shield tunneling, segments of a tunnel are assigned along to a predetermined curve called the planning line. Conventionally, skilled engineers manually assign the segments to minimize gaps between each segment and the planning line. Nevertheless, we have only to reduce each gap less than a tolerance, and there is a demand to reduce the amount of soil excavated along to the segments. Handling the reducing gaps as constraints and reducing the amount of excavated soil as an objective, this paper addresses the segment assignment as a constrained combinatorial optimization problem. These constraints are severe, and the problem has an extremely narrow feasible region. For this problem, we proposed the ε constrained integer categorical particle swarm optimization (εICPSO), adapting a constraint handling method called the ε constrained method to the integer categorical particle swarm optimization. The effectiveness of the εICPSO to the segment assignment is shown by the two-dimensional simulator experiment using real construction data. The experimental results show that the proposed method has a potential to reduce the amount of excavated soil as compared to the conventional method (skilled engineer) while keeping the all gaps between segments and the planning line falling within the tolerance. The εICPSO statistically performed the best in all the test problems.

A PSO based Approach to Assign Segments for Reducing Excavated Soil in Shield Tunneling